Abstract:
Epilepsy is a neurological disorder which affects the electrical activity of the brain
resulting in seizures with various intensities. The detection of epileptic seizure is of
utmost importance to prevent any serious injuries or death to patients. Applications of
Human Activity Recognition (HAR) in healthcare for continuous monitoring of
Activities of Daily Livings (ADLs) can assist in the detection of abnormalities that may
indicate the prevalence of neurological disorders such as Alzheimer’s, stroke, and
epileptic seizures etc. Wearable sensors such as accelerometer embedded in smart
phones and EEG headsets etc. can be used for continuous monitoring of ADLs. This
research aims to detect seizures and neuroelectric abnormalities in epileptic patient
using wearable EEG headset Neurosky MindWave. The data files are recorded in an
un-constraint environment from healthy volunteers and epileptic patients. These data
files are pre-processed using time and frequency domain analysis and machine learning
classifiers are applied for classification of activities into stationary, light ambulatory,
intense ambulatory, and abnormal activities. The proposed method is then compared
against constraint environment EEG dataset from Bonn University and dataset collected
from accelerometer-based application ‘MyNeuroHealth’. It is evident from the research
that portable EEG headset may be employed to detect abnormalities which may lead to
a seizure whereas the system implemented by ACM is restricted to detect motor
components only. The results show that SVM classifier can detect ADLs and seizure
with a reasonable accuracy of 96.7% in an un-constraint environment where Random
Forest performs better for classification of states in a constraint environment with 50%
greater accuracy than SVM classifier. Furthermore, the proposed and implemented
EEG-based system can detect ADLs and seizure with 3% better accuracy as compared
to accelerometer-based system.
